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In this paper, we introduce a novel algorithmic framework for sub-diffractive super-resolution imaging of dynamic, time varying targets. We extend recent works in low rank Fourier ptychographic imaging, to incorporate model-correction schemes, which correct for errors propagated due to inaccuracies in fitting an exact low rank model to the target video acquired. Through our algorithm, we are able to demonstrate superior reconstruction quality of video from phaseless Fourier ptychographic measurements, at low sample complexities, as compared to conventional ptychographic setups.

Developing recommendation system for fashion images is challenging due to the inherent ambiguity associated with what criterion a user is looking at. Suggesting multiple images where each output image is similar to the query image on the basis of a different feature or part is one way to mitigate the problem. Existing works for fashion recommendation have used Siamese or Triplet network to learn features between a similar pair and a similar dissimilar triplet respectively.